GaussianMOT

Joint 3D object detection and tracking on a camera–radar Gaussian BEV. Code: https://github.com/KimJunHan/GaussianMOT

This repository holds the checkpoint reported in the paper (weights only, end of the 24th training epoch).

File	Size	SHA-256
gaussianmot_epoch23.ckpt	368 MB	d3e3a5e55265b878420dbbcf1d6868e47350e8c968fb5061e2aad3b88af5dd23

Results (nuScenes val, inference seed 0)

Detection		Tracking
mAP	35.0	AMOTA	0.348
NDS	42.5	AMOTP	1.31
mATE	0.539	Recall	0.411
mASE	0.307	ID-S	897
mAOE	0.681
mAVE	0.511
mAAE	0.465

6 cameras + 5 radars, 448×800 input, 6.5 FPS on an RTX 3090 (batch size 1).

Usage

git clone https://github.com/KimJunHan/GaussianMOT && cd GaussianMOT
hf download kimjunhan/GaussianMOT gaussianmot_epoch23.ckpt --local-dir ckpt
python tools/evaluate.py checkpoint_path=ckpt/gaussianmot_epoch23.ckpt device=cuda

The file contains state_dict only (no optimizer state); it loads with tools/evaluate.py as is. Densification draws random shifts at inference, so results depend on eval_seed (default 0) in the last digit.